This presentation shows the number of cumulative cases of COVID-19 per country as provided by the Johns Hopkins University Center for Systems Science and Engineering.

The primary data cleaning required was transforming the data to the "longer form" consistent with tidy data practices.

tidyConfirmedCases <- confirmedCases %>%
        pivot_longer(-c(Province.State, Country.Region, Lat, Long), 
                names_to = "Date", values_to = "cumulativeCases") %>%
        mutate(Date = substring(Date, 2))%>%
        mutate(Date = mdy(Date,tz="UTC"))%>%
        select(Date,Country.Region, everything())%>%
        arrange(Date,Country.Region,Province.State)%>%
        rename(lat = Lat, lng = Long )
head(tidyConfirmedCases)

## # A tibble: 6 x 6
##   Date                Country.Region Province.State   lat    lng
##   <dttm>              <fct>          <fct>          <dbl>  <dbl>
## 1 2020-01-22 00:00:00 Afghanistan    ""              33    65   
## 2 2020-01-22 00:00:00 Albania        ""              41.2  20.2 
## 3 2020-01-22 00:00:00 Algeria        ""              28.0   1.66
## 4 2020-01-22 00:00:00 Andorra        ""              42.5   1.52
## 5 2020-01-22 00:00:00 Angola         ""             -11.2  17.9 
## 6 2020-01-22 00:00:00 Antigua and B~ ""              17.1 -61.8 
## # ... with 1 more variable: cumulativeCases <int>

The current cases are captured by the interactive map below:

Finally, we have an interactive plot showing country specific growth.